Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "34" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 49 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 47 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459864 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 25.439825 | 5.421480 | 8.202844 | 11.016412 | 8.239873 | 1.019112 | 3.243979 | -0.072523 | 0.0402 | 0.6515 | 0.5464 | nan | nan |
| 2459863 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 15.497850 | 2.039172 | 2.643168 | -0.252139 | 2.993942 | 3.802171 | 1.337655 | -0.831860 | 0.0409 | 0.6450 | 0.5400 | nan | nan |
| 2459862 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 15.339473 | 2.954082 | 8.905305 | 13.356398 | 12.482944 | 2.685019 | 1.017191 | -0.780173 | 0.0455 | 0.6795 | 0.5273 | nan | nan |
| 2459861 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.830891 | 1.122095 | 2.886264 | -1.894759 | 2.546711 | 3.270494 | 1.024776 | -0.354363 | 0.0397 | 0.6577 | 0.5068 | nan | nan |
| 2459860 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.830820 | 1.959494 | 8.468623 | 7.718041 | 14.681356 | 2.254181 | 1.304324 | -0.805962 | 0.0408 | 0.6493 | 0.5552 | nan | nan |
| 2459859 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.788176 | 0.894657 | 3.347653 | -2.390551 | 2.240509 | 2.951603 | 0.337352 | -0.741215 | 0.0434 | 0.6553 | 0.5461 | nan | nan |
| 2459858 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.729288 | 0.814840 | 3.372854 | -2.894663 | 2.287258 | 8.237116 | 1.007570 | 1.526418 | 0.0416 | 0.6597 | 0.5473 | 1.241931 | 2.881606 |
| 2459857 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 5.029107 | 0.585648 | 0.250278 | 1.362252 | 0.734939 | 3.721368 | 3.302402 | 0.708987 | 0.0290 | 0.0289 | 0.0006 | nan | nan |
| 2459856 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 17.278313 | 3.031357 | 8.287908 | 6.437786 | 6.652525 | 2.840174 | 2.034517 | -0.859082 | 0.0440 | 0.6735 | 0.5689 | 1.244955 | 2.562242 |
| 2459855 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 18.202161 | 3.546594 | 7.784330 | 8.139510 | 2.684589 | 8.430062 | 0.507626 | -0.923274 | 0.0436 | 0.6853 | 0.5829 | 1.220033 | 3.047257 |
| 2459854 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 18.230710 | 3.150288 | 5.606733 | 6.605160 | 3.839472 | 3.606942 | 2.815106 | 0.432287 | 0.0442 | 0.7206 | 0.6015 | 1.231065 | 3.017055 |
| 2459853 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 15.115139 | 2.708840 | 8.419716 | 8.423114 | 7.037320 | 4.055470 | 2.456752 | 0.055527 | 0.0447 | 0.6602 | 0.5507 | 1.242437 | 3.383687 |
| 2459852 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 13.817689 | 4.602917 | 9.199763 | 10.414589 | 15.603567 | 3.644895 | 16.692812 | 8.429692 | 0.0416 | 0.8093 | 0.6659 | 1.214775 | 5.442199 |
| 2459851 | not_connected | 100.00% | 90.23% | 0.00% | 0.00% | 100.00% | 0.00% | 12.017169 | 4.397950 | 8.546470 | 9.720896 | 21.468027 | 10.339538 | 12.387225 | 7.504056 | 0.0988 | 0.7138 | 0.4942 | 0.866643 | 2.079185 |
| 2459850 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 14.495922 | 3.153455 | 7.424956 | 7.830817 | 10.308146 | 4.237785 | 5.394209 | 2.879895 | 0.0484 | 0.7301 | 0.5719 | 1.265949 | 3.091004 |
| 2459849 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 16.757320 | 2.806751 | 16.268505 | 15.011370 | 7.128088 | 4.315881 | 2.966135 | -0.358714 | 0.0428 | 0.7206 | 0.5744 | 1.270871 | 3.494749 |
| 2459848 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 15.496630 | 2.959010 | 8.518637 | 11.830365 | 14.319080 | 3.782307 | 1.121389 | -0.811380 | 0.0430 | 0.7271 | 0.5836 | 1.253355 | 3.210068 |
| 2459847 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 17.713985 | 3.075747 | 7.683540 | 12.145552 | 21.779563 | 6.416233 | 0.036489 | -0.836231 | 0.0376 | 0.6601 | 0.5313 | 1.156511 | 3.019124 |
| 2459846 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 27.416778 | 6.596305 | 14.777133 | 10.385713 | 21.659479 | 1.862991 | 3.587379 | -0.443634 | 0.0369 | 0.6607 | 0.5103 | 0.812346 | 1.397902 |
| 2459845 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 19.540275 | 3.705230 | 12.631284 | 14.952011 | 10.103377 | 3.448940 | 0.583008 | -1.311327 | 0.0484 | 0.7333 | 0.5825 | 0.000000 | 0.000000 |
| 2459844 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.471518 | 3.143226 | 51.369139 | 55.946271 | 0.349979 | 4.944669 | 2.372725 | 1.034953 | 0.0282 | 0.0286 | 0.0009 | nan | nan |
| 2459843 | not_connected | 100.00% | 100.00% | 0.66% | 0.00% | 100.00% | 0.00% | 20.020080 | 6.360883 | 6.017662 | 15.026662 | 64.263762 | 4.610992 | 0.189582 | -2.710582 | 0.0426 | 0.7389 | 0.5775 | 0.000000 | 0.000000 |
| 2459842 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.945509 | 1.569544 | 3.582351 | -0.164784 | -0.105533 | -1.720372 | 0.425890 | -2.246716 | 0.0487 | 0.6095 | 0.4343 | 1.263186 | 3.372820 |
| 2459841 | not_connected | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.599907 | 44.902973 | 32.519816 | 122.025821 | 51.116090 | 97.172070 | 22.255647 | 8.216420 | 0.0466 | 0.7384 | 0.3532 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 25.439825 | 5.421480 | 25.439825 | 11.016412 | 8.202844 | 1.019112 | 8.239873 | -0.072523 | 3.243979 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 15.497850 | 15.497850 | 2.039172 | 2.643168 | -0.252139 | 2.993942 | 3.802171 | 1.337655 | -0.831860 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 15.339473 | 15.339473 | 2.954082 | 8.905305 | 13.356398 | 12.482944 | 2.685019 | 1.017191 | -0.780173 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 11.830891 | 1.122095 | 11.830891 | -1.894759 | 2.886264 | 3.270494 | 2.546711 | -0.354363 | 1.024776 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Temporal Variability | 14.681356 | 12.830820 | 1.959494 | 8.468623 | 7.718041 | 14.681356 | 2.254181 | 1.304324 | -0.805962 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 10.788176 | 10.788176 | 0.894657 | 3.347653 | -2.390551 | 2.240509 | 2.951603 | 0.337352 | -0.741215 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 11.729288 | 0.814840 | 11.729288 | -2.894663 | 3.372854 | 8.237116 | 2.287258 | 1.526418 | 1.007570 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 5.029107 | 0.585648 | 5.029107 | 1.362252 | 0.250278 | 3.721368 | 0.734939 | 0.708987 | 3.302402 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 17.278313 | 17.278313 | 3.031357 | 8.287908 | 6.437786 | 6.652525 | 2.840174 | 2.034517 | -0.859082 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 18.202161 | 3.546594 | 18.202161 | 8.139510 | 7.784330 | 8.430062 | 2.684589 | -0.923274 | 0.507626 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 18.230710 | 3.150288 | 18.230710 | 6.605160 | 5.606733 | 3.606942 | 3.839472 | 0.432287 | 2.815106 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 15.115139 | 2.708840 | 15.115139 | 8.423114 | 8.419716 | 4.055470 | 7.037320 | 0.055527 | 2.456752 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Temporal Discontinuties | 16.692812 | 13.817689 | 4.602917 | 9.199763 | 10.414589 | 15.603567 | 3.644895 | 16.692812 | 8.429692 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Temporal Variability | 21.468027 | 12.017169 | 4.397950 | 8.546470 | 9.720896 | 21.468027 | 10.339538 | 12.387225 | 7.504056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 14.495922 | 14.495922 | 3.153455 | 7.424956 | 7.830817 | 10.308146 | 4.237785 | 5.394209 | 2.879895 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 16.757320 | 16.757320 | 2.806751 | 16.268505 | 15.011370 | 7.128088 | 4.315881 | 2.966135 | -0.358714 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 15.496630 | 2.959010 | 15.496630 | 11.830365 | 8.518637 | 3.782307 | 14.319080 | -0.811380 | 1.121389 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Temporal Variability | 21.779563 | 3.075747 | 17.713985 | 12.145552 | 7.683540 | 6.416233 | 21.779563 | -0.836231 | 0.036489 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 27.416778 | 27.416778 | 6.596305 | 14.777133 | 10.385713 | 21.659479 | 1.862991 | 3.587379 | -0.443634 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 19.540275 | 3.705230 | 19.540275 | 14.952011 | 12.631284 | 3.448940 | 10.103377 | -1.311327 | 0.583008 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Power | 55.946271 | 10.471518 | 3.143226 | 51.369139 | 55.946271 | 0.349979 | 4.944669 | 2.372725 | 1.034953 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Temporal Variability | 64.263762 | 6.360883 | 20.020080 | 15.026662 | 6.017662 | 4.610992 | 64.263762 | -2.710582 | 0.189582 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | ee Shape | 11.945509 | 11.945509 | 1.569544 | 3.582351 | -0.164784 | -0.105533 | -1.720372 | 0.425890 | -2.246716 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | N06 | not_connected | nn Power | 122.025821 | 10.599907 | 44.902973 | 32.519816 | 122.025821 | 51.116090 | 97.172070 | 22.255647 | 8.216420 |